A guide to machine learning for bacterial host attribution using genome sequence data.

ABSTRACT: With the ever-expanding number of available sequences from bacterial genomes, and the expectation that this data type will be the primary one generated from both diagnostic and research laboratories for the foreseeable future, then there is both an opportunity and a need to evaluate how effectively computational approaches can be used within bacterial genomics to predict and understand complex phenotypes, such as pathogenic potential and host source. This article applied various quantitative methods such as diversity indexes, pangenome-wide association studies (GWAS) and dimensionality reduction techniques to better understand the data and then compared how well unsupervised and supervised machine learning (ML) methods could predict the source host of the isolates. The study uses the examp

SUBMITTER: Lupolova N 

PROVIDER: S-EPMC6939162 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature